arXiv:2610. 13924v1 Announce Type: new Abstract: A parallel sampler can choose at random which coordinates to generate together.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607.17469v2 Announce Type: replace-cross Abstract: How much does an algorithm's running-time distribution under independent randomness reveal about its behavior when independence is no longer...
The paper introduces a new framework for adaptive parallel sampling of discrete vectors, where a deterministic policy reveals coordinates round‑by‑round based on previously observed values and samples the remaining coordinates from their exact conditional marginals. The authors prove an exact identity linking the forward Kullback‑Leibler divergence of any policy to the expected conditional total correlation accumulated during the sampling process, establishing conditional total correlation as the precise information cost of within‑round parallelism. Using this identity, they derive zero‑error schedules for finite‑order Markov chains, characterize the serial depth of Bernoulli walks, and demonstrate separations between different reveal orders, permutation strategies, and string structures, thereby revealing how conditional dependence governs parallelizability. whyItMatters":"The results provide a principled, information‑theoretic measure of parallel sampling efficiency that can guide the design of decoding rules for masked diffusion models and other generative systems."
arXiv:2609.37841v1 Announce Type: new Abstract: Masked generative models offer parallel token prediction, but accurate parallel sampling must account for dependencies among tokens. When dependencies...
arXiv:2609. 08564v1 Announce Type: cross Abstract: We study distributed one-dimensional mean estimation under a 1-bit communication constraint.
arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.
arXiv:2608. 06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.